Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/annoviko/pyclustering

pyclustering is a Python, C++ data mining library.
https://github.com/annoviko/pyclustering
algorithms c-plus-plus clustering data-mining data-science machine-learning neural-networks oscillatory-networks python python3
Last synced: 25 days ago
JSON representation
pyclustering is a Python, C++ data mining library.
Host: GitHub
URL: https://github.com/annoviko/pyclustering
Owner: annoviko
License: bsd-3-clause
Created: 2014-02-25T18:59:03.000Z (over 10 years ago)
Default Branch: master
Last Pushed: 2024-02-25T11:40:08.000Z (9 months ago)
Last Synced: 2024-09-25T23:31:02.240Z (about 1 month ago)
Topics: algorithms, c-plus-plus, clustering, data-mining, data-science, machine-learning, neural-networks, oscillatory-networks, python, python3
Language: Python
Homepage: https://pyclustering.github.io/
Size: 33.4 MB
Stars: 1,165
Watchers: 41
Forks: 249
Open Issues: 76
Metadata Files:
- Readme: README.rst
- Changelog: CHANGES
- License: LICENSE
Awesome Lists containing this project

awesome-python-machine-learning-resources - GitHub - 9% open · ⏱️ 12.02.2021): (Others)
README

        Warning - Attention Users

=========================

**Please be aware that the `pyclustering` library is no longer supported as of 2021 due to personal reasons. There will be no further maintenance, issue addressing, or feature development for this repository.**

**For continued usage, I recommend seeking alternative solutions.**

**Thank you for your understanding.**

Build Status

============

|Build Status Linux MacOS| |Build Status Win| |Coverage Status| |PyPi| |Download Counter| |JOSS|

PyClustering

============

**pyclustering** is a Python, C++ data mining library (clustering

algorithm, oscillatory networks, neural networks). The library provides

Python and C++ implementations (C++ pyclustering library) of each algorithm or

model. C++ pyclustering library is a part of pyclustering and supported for

Linux, Windows and MacOS operating systems.

**Version**: 0.11.dev

**License**: The 3-Clause BSD License

**E-Mail**: [email protected]

**Documentation**: https://pyclustering.github.io/docs/0.10.1/html/

**Homepage**: https://pyclustering.github.io/

**PyClustering Wiki**: https://github.com/annoviko/pyclustering/wiki

Dependencies

============

**Required packages**: scipy, matplotlib, numpy, Pillow

**Python version**: >=3.6 (32-bit, 64-bit)

**C++ version**: >= 14 (32-bit, 64-bit)

Performance

===========

Each algorithm is implemented using Python and C/C++ language, if your platform is not supported then Python

implementation is used, otherwise C/C++. Implementation can be chosen by `ccore` flag (by default it is always

'True' and it means that C/C++ is used), for example:

.. code:: python

    # As by default - C/C++ part of the library is used

    xmeans_instance_1 = xmeans(data_points, start_centers, 20, ccore=True);

    # The same - C/C++ part of the library is used by default

    xmeans_instance_2 = xmeans(data_points, start_centers, 20);

    # Switch off core - Python is used

    xmeans_instance_3 = xmeans(data_points, start_centers, 20, ccore=False);

Installation

============

Installation using pip3 tool:

.. code:: bash

    $ pip3 install pyclustering

Manual installation from official repository using Makefile:

.. code:: bash

    # get sources of the pyclustering library, for example, from repository

    $ mkdir pyclustering

    $ cd pyclustering/

    $ git clone https://github.com/annoviko/pyclustering.git .

    # compile CCORE library (core of the pyclustering library).

    $ cd ccore/

    $ make ccore_64bit      # build for 64-bit OS

    # $ make ccore_32bit    # build for 32-bit OS

    # return to parent folder of the pyclustering library

    $ cd ../

    # install pyclustering library

    $ python3 setup.py install

    # optionally - test the library

    $ python3 setup.py test

Manual installation using CMake:

.. code:: bash

    # get sources of the pyclustering library, for example, from repository

    $ mkdir pyclustering

    $ cd pyclustering/

    $ git clone https://github.com/annoviko/pyclustering.git .

    # generate build files.

    $ mkdir build

    $ cmake ..

    # build pyclustering-shared target depending on what was generated (Makefile or MSVC solution)

    # if Makefile has been generated then

    $ make pyclustering-shared

    # return to parent folder of the pyclustering library

    $ cd ../

    # install pyclustering library

    $ python3 setup.py install

    # optionally - test the library

    $ python3 setup.py test

Manual installation using Microsoft Visual Studio solution:

1. Clone repository from: https://github.com/annoviko/pyclustering.git

2. Open folder `pyclustering/ccore`

3. Open Visual Studio project `ccore.sln`

4. Select solution platform: `x86` or `x64`

5. Build `pyclustering-shared` project.

6. Add pyclustering folder to python path or install it using setup.py

.. code:: bash

    # install pyclustering library

    $ python3 setup.py install

    # optionally - test the library

    $ python3 setup.py test

Proposals, Questions, Bugs

==========================

In case of any questions, proposals or bugs related to the pyclustering please contact to [email protected] or create an issue here.

PyClustering Status

===================

+----------------------+------------------------------+-------------------------------------+---------------------------------+

| Branch               | master                       | 0.10.dev                            | 0.10.1.rel                      |

+======================+==============================+=====================================+=================================+

| Build (Linux, MacOS) | |Build Status Linux MacOS|   | |Build Status Linux MacOS 0.10.dev| | |Build Status Linux 0.10.1.rel| |

+----------------------+------------------------------+-------------------------------------+---------------------------------+

| Build (Win)          | |Build Status Win|           | |Build Status Win 0.10.dev|         | |Build Status Win 0.10.1.rel|   |

+----------------------+------------------------------+-------------------------------------+---------------------------------+

| Code Coverage        | |Coverage Status|            | |Coverage Status 0.10.dev|          | |Coverage Status 0.10.1.rel|    |

+----------------------+------------------------------+-------------------------------------+---------------------------------+

Cite the Library

================

If you are using pyclustering library in a scientific paper, please, cite the library:

Novikov, A., 2019. PyClustering: Data Mining Library. Journal of Open Source Software, 4(36), p.1230. Available at: http://dx.doi.org/10.21105/joss.01230.

BibTeX entry:

.. code::

    @article{Novikov2019,

        doi         = {10.21105/joss.01230},

        url         = {https://doi.org/10.21105/joss.01230},

        year        = 2019,

        month       = {apr},

        publisher   = {The Open Journal},

        volume      = {4},

        number      = {36},

        pages       = {1230},

        author      = {Andrei Novikov},

        title       = {{PyClustering}: Data Mining Library},

        journal     = {Journal of Open Source Software}

    }

Brief Overview of the Library Content

=====================================

**Clustering algorithms and methods (module pyclustering.cluster):**

+------------------------+---------+-----+

| Algorithm              | Python  | C++ |

+========================+=========+=====+

| Agglomerative          | ✓       | ✓   |

+------------------------+---------+-----+

| BANG                   | ✓       |     |

+------------------------+---------+-----+

| BIRCH                  | ✓       |     |

+------------------------+---------+-----+

| BSAS                   | ✓       | ✓   |

+------------------------+---------+-----+

| CLARANS                | ✓       |     |

+------------------------+---------+-----+

| CLIQUE                 | ✓       | ✓   |

+------------------------+---------+-----+

| CURE                   | ✓       | ✓   |

+------------------------+---------+-----+

| DBSCAN                 | ✓       | ✓   |

+------------------------+---------+-----+

| Elbow                  | ✓       | ✓   |

+------------------------+---------+-----+

| EMA                    | ✓       |     |

+------------------------+---------+-----+

| Fuzzy C-Means          | ✓       | ✓   |

+------------------------+---------+-----+

| GA (Genetic Algorithm) | ✓       | ✓   |

+------------------------+---------+-----+

| G-Means                | ✓       | ✓   |

+------------------------+---------+-----+

| HSyncNet               | ✓       | ✓   |

+------------------------+---------+-----+

| K-Means                | ✓       | ✓   |

+------------------------+---------+-----+

| K-Means++              | ✓       | ✓   |

+------------------------+---------+-----+

| K-Medians              | ✓       | ✓   |

+------------------------+---------+-----+

| K-Medoids              | ✓       | ✓   |

+------------------------+---------+-----+

| MBSAS                  | ✓       | ✓   |

+------------------------+---------+-----+

| OPTICS                 | ✓       | ✓   |

+------------------------+---------+-----+

| ROCK                   | ✓       | ✓   |

+------------------------+---------+-----+

| Silhouette             | ✓       | ✓   |

+------------------------+---------+-----+

| SOM-SC                 | ✓       | ✓   |

+------------------------+---------+-----+

| SyncNet                | ✓       | ✓   |

+------------------------+---------+-----+

| Sync-SOM               | ✓       |     |

+------------------------+---------+-----+

| TTSAS                  | ✓       | ✓   |

+------------------------+---------+-----+

| X-Means                | ✓       | ✓   |

+------------------------+---------+-----+

**Oscillatory networks and neural networks (module pyclustering.nnet):**

+--------------------------------------------------------------------------------+---------+-----+

| Model                                                                          | Python  | C++ |

+================================================================================+=========+=====+

| CNN (Chaotic Neural Network)                                                   | ✓       |     |

+--------------------------------------------------------------------------------+---------+-----+

| fSync (Oscillatory network based on Landau-Stuart equation and Kuramoto model) | ✓       |     |

+--------------------------------------------------------------------------------+---------+-----+

| HHN (Oscillatory network based on Hodgkin-Huxley model)                        | ✓       | ✓   |

+--------------------------------------------------------------------------------+---------+-----+

| Hysteresis Oscillatory Network                                                 | ✓       |     |

+--------------------------------------------------------------------------------+---------+-----+

| LEGION (Local Excitatory Global Inhibitory Oscillatory Network)                | ✓       | ✓   |

+--------------------------------------------------------------------------------+---------+-----+

| PCNN (Pulse-Coupled Neural Network)                                            | ✓       | ✓   |

+--------------------------------------------------------------------------------+---------+-----+

| SOM (Self-Organized Map)                                                       | ✓       | ✓   |

+--------------------------------------------------------------------------------+---------+-----+

| Sync (Oscillatory network based on Kuramoto model)                             | ✓       | ✓   |

+--------------------------------------------------------------------------------+---------+-----+

| SyncPR (Oscillatory network for pattern recognition)                           | ✓       | ✓   |

+--------------------------------------------------------------------------------+---------+-----+

| SyncSegm (Oscillatory network for image segmentation)                          | ✓       | ✓   |

+--------------------------------------------------------------------------------+---------+-----+

**Graph Coloring Algorithms (module pyclustering.gcolor):**

+------------------------+---------+-----+

| Algorithm              | Python  | C++ |

+========================+=========+=====+

| DSatur                 | ✓       |     |

+------------------------+---------+-----+

| Hysteresis             | ✓       |     |

+------------------------+---------+-----+

| GColorSync             | ✓       |     |

+------------------------+---------+-----+

**Containers (module pyclustering.container):**

+------------------------+---------+-----+

| Algorithm              | Python  | C++ |

+========================+=========+=====+

| KD Tree                | ✓       | ✓   |

+------------------------+---------+-----+

| CF Tree                | ✓       |     |

+------------------------+---------+-----+

Examples in the Library

=======================

The library contains examples for each algorithm and oscillatory network model:

**Clustering examples:** ``pyclustering/cluster/examples``

**Graph coloring examples:** ``pyclustering/gcolor/examples``

**Oscillatory network examples:** ``pyclustering/nnet/examples``

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/example_cluster_place.png

   :alt: Where are examples?

Code Examples

=============

**Data clustering by CURE algorithm**

.. code:: python

    from pyclustering.cluster import cluster_visualizer;

    from pyclustering.cluster.cure import cure;

    from pyclustering.utils import read_sample;

    from pyclustering.samples.definitions import FCPS_SAMPLES;

    # Input data in following format [ [0.1, 0.5], [0.3, 0.1], ... ].

    input_data = read_sample(FCPS_SAMPLES.SAMPLE_LSUN);

    # Allocate three clusters.

    cure_instance = cure(input_data, 3);

    cure_instance.process();

    clusters = cure_instance.get_clusters();

    # Visualize allocated clusters.

    visualizer = cluster_visualizer();

    visualizer.append_clusters(clusters, input_data);

    visualizer.show();

**Data clustering by K-Means algorithm**

.. code:: python

    from pyclustering.cluster.kmeans import kmeans, kmeans_visualizer

    from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer

    from pyclustering.samples.definitions import FCPS_SAMPLES

    from pyclustering.utils import read_sample

    # Load list of points for cluster analysis.

    sample = read_sample(FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)

    # Prepare initial centers using K-Means++ method.

    initial_centers = kmeans_plusplus_initializer(sample, 2).initialize()

    # Create instance of K-Means algorithm with prepared centers.

    kmeans_instance = kmeans(sample, initial_centers)

    # Run cluster analysis and obtain results.

    kmeans_instance.process()

    clusters = kmeans_instance.get_clusters()

    final_centers = kmeans_instance.get_centers()

    # Visualize obtained results

    kmeans_visualizer.show_clusters(sample, clusters, final_centers)

**Data clustering by OPTICS algorithm**

.. code:: python

    from pyclustering.cluster import cluster_visualizer

    from pyclustering.cluster.optics import optics, ordering_analyser, ordering_visualizer

    from pyclustering.samples.definitions import FCPS_SAMPLES

    from pyclustering.utils import read_sample

    # Read sample for clustering from some file

    sample = read_sample(FCPS_SAMPLES.SAMPLE_LSUN)

    # Run cluster analysis where connectivity radius is bigger than real

    radius = 2.0

    neighbors = 3

    amount_of_clusters = 3

    optics_instance = optics(sample, radius, neighbors, amount_of_clusters)

    # Performs cluster analysis

    optics_instance.process()

    # Obtain results of clustering

    clusters = optics_instance.get_clusters()

    noise = optics_instance.get_noise()

    ordering = optics_instance.get_ordering()

    # Visualize ordering diagram

    analyser = ordering_analyser(ordering)

    ordering_visualizer.show_ordering_diagram(analyser, amount_of_clusters)

    # Visualize clustering results

    visualizer = cluster_visualizer()

    visualizer.append_clusters(clusters, sample)

    visualizer.show()

**Simulation of oscillatory network PCNN**

.. code:: python

    from pyclustering.nnet.pcnn import pcnn_network, pcnn_visualizer

    # Create Pulse-Coupled neural network with 10 oscillators.

    net = pcnn_network(10)

    # Perform simulation during 100 steps using binary external stimulus.

    dynamic = net.simulate(50, [1, 1, 1, 0, 0, 0, 0, 1, 1, 1])

    # Allocate synchronous ensembles from the output dynamic.

    ensembles = dynamic.allocate_sync_ensembles()

    # Show output dynamic.

    pcnn_visualizer.show_output_dynamic(dynamic, ensembles)

**Simulation of chaotic neural network CNN**

.. code:: python

    from pyclustering.cluster import cluster_visualizer

    from pyclustering.samples.definitions import SIMPLE_SAMPLES

    from pyclustering.utils import read_sample

    from pyclustering.nnet.cnn import cnn_network, cnn_visualizer

    # Load stimulus from file.

    stimulus = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)

    # Create chaotic neural network, amount of neurons should be equal to amount of stimulus.

    network_instance = cnn_network(len(stimulus))

    # Perform simulation during 100 steps.

    steps = 100

    output_dynamic = network_instance.simulate(steps, stimulus)

    # Display output dynamic of the network.

    cnn_visualizer.show_output_dynamic(output_dynamic)

    # Display dynamic matrix and observation matrix to show clustering phenomenon.

    cnn_visualizer.show_dynamic_matrix(output_dynamic)

    cnn_visualizer.show_observation_matrix(output_dynamic)

    # Visualize clustering results.

    clusters = output_dynamic.allocate_sync_ensembles(10)

    visualizer = cluster_visualizer()

    visualizer.append_clusters(clusters, stimulus)

    visualizer.show()

Illustrations

=============

**Cluster allocation on FCPS dataset collection by DBSCAN:**

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/fcps_cluster_analysis.png

   :alt: Clustering by DBSCAN

**Cluster allocation by OPTICS using cluster-ordering diagram:**

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/optics_example_clustering.png

   :alt: Clustering by OPTICS

**Partial synchronization (clustering) in Sync oscillatory network:**

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/sync_partial_synchronization.png

   :alt: Partial synchronization in Sync oscillatory network

**Cluster visualization by SOM (Self-Organized Feature Map)**

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/target_som_processing.png

   :alt: Cluster visualization by SOM

.. |Build Status Linux MacOS| image:: https://travis-ci.org/annoviko/pyclustering.svg?branch=master

   :target: https://travis-ci.org/annoviko/pyclustering

.. |Build Status Win| image:: https://ci.appveyor.com/api/projects/status/4uly2exfp49emwn0/branch/master?svg=true

   :target: https://ci.appveyor.com/project/annoviko/pyclustering/branch/master

.. |Coverage Status| image:: https://coveralls.io/repos/github/annoviko/pyclustering/badge.svg?branch=master&ts=1

   :target: https://coveralls.io/github/annoviko/pyclustering?branch=master

.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4280556.svg

   :target: https://doi.org/10.5281/zenodo.4280556

.. |PyPi| image:: https://badge.fury.io/py/pyclustering.svg

   :target: https://badge.fury.io/py/pyclustering

.. |Build Status Linux MacOS 0.10.dev| image:: https://travis-ci.org/annoviko/pyclustering.svg?branch=0.10.dev

   :target: https://travis-ci.org/annoviko/pyclustering

.. |Build Status Win 0.10.dev| image:: https://ci.appveyor.com/api/projects/status/4uly2exfp49emwn0/branch/0.10.dev?svg=true

   :target: https://ci.appveyor.com/project/annoviko/pyclustering/branch/0.9.dev

.. |Coverage Status 0.10.dev| image:: https://coveralls.io/repos/github/annoviko/pyclustering/badge.svg?branch=0.10.dev&ts=1

   :target: https://coveralls.io/github/annoviko/pyclustering?branch=0.9.dev

.. |Build Status Linux 0.10.1.rel| image:: https://travis-ci.org/annoviko/pyclustering.svg?branch=0.10.1.rel

   :target: https://travis-ci.org/annoviko/pyclustering

.. |Build Status Win 0.10.1.rel| image:: https://ci.appveyor.com/api/projects/status/4uly2exfp49emwn0/branch/0.10.1.rel?svg=true

   :target: https://ci.appveyor.com/project/annoviko/pyclustering/branch/0.10.1.rel

.. |Coverage Status 0.10.1.rel| image:: https://coveralls.io/repos/github/annoviko/pyclustering/badge.svg?branch=0.10.1.rel&ts=1

   :target: https://coveralls.io/github/annoviko/pyclustering?branch=0.10.1.rel

.. |Download Counter| image:: https://pepy.tech/badge/pyclustering

   :target: https://pepy.tech/project/pyclustering

.. |JOSS| image:: http://joss.theoj.org/papers/10.21105/joss.01230/status.svg

   :target: https://doi.org/10.21105/joss.01230